Psychosocial factors influencing risk-taking in middle age for STIs

Objectives To increase the knowledge of the psychosocial factors influencing sexual risk-taking for STIs among adults in late middle age. Methods Individual interviews were conducted either face to face or by telephone with 31 heterosexual men and women aged between 45 and 65. They were recruited from NHS sexual health services (n=16) and council run culture and leisure facilities (n=15) in a large Scottish city. A total of 18 women and 13 men were interviewed. All interviews were transcribed in full and thematically analysed. Results Analysis detailed important psychosocial and sociocultural factors; the prioritisation of intimacy above and beyond concerns about risks for STI in sexual partnerships; the importance of unwanted pregnancy in shaping risk perceptions throughout the life course; vulnerability associated with periods of relationship transition (eg, bereavement, divorce or separation); social norms and cultural expectations relating to age-appropriate sexual and health-seeking behaviours. Conclusions This is the first qualitative study to examine the factors associated with sexual risk-taking among heterosexual adults in late middle age in the UK. Many factors associated with sexual risk-taking are similar to those reported within other populations. However, we also detail population-specific factors, which should be considered in terms of the development of interventions for ‘at risk’ older adults, or the tailoring of wider behaviour change interventions to this specific age group

Net gen or not gen? Student and Staff Evaluations of the use of Podcasts/Audio Files and an Electronic Voting System (EVS) in a Blended Learning Module

Abstract: At the authors" institution, blended learning is defined as "educational provision where high quality e-learning opportunities and excellent campus-based learning are combined or blended in coherent, reflective and innovative ways so that learning is enhanced and choice is increased. Students are at the centre of this vision". This paper outlines work undertaken to investigate the impact of integrating podcasts/audio file downloads and use of an electronic voting system (EVS) to transform module delivery from a traditional mode to a blended delivery. The purpose being to introduce a measure of flexibility in how, when and where students study; to increase interactivity and engagement in classroom sessions, and to enhance students' learning. The student cohort is diverse in respect of age -the majority or students are direct entry students of the so-called net generation, whilst a significant number of students (35%) are mature students. Would age be an influencing factor on the students" preference for the learning methods employed, or their willingness or ability to engage with the technologies? An interim student evaluation was undertaken at the midpoint of the taught module, to provide formative, illustrative data to the module leader and teaching team about student opinion of the teaching methods and learning technologies. Given the option of returning to the traditional delivery method, 77.5% of students either "agreed" or "strongly agreed" that the module should continue to run in its blended format. The final evaluation discovered no discernable differences in the behaviour of the direct entry students compared to the mature students. Both groups accessed the podcasts easily, generally at home, and spent longer than if blended learning technologies had not been used. It was discovered that 16% of the mature and 24% of the direct entry students would have preferred lectures to podcasts, although the students were positive about the flexibility offered. Both groups of students were virtually unanimous on the benefits of the EVS to support learning. The teaching team concluded that the blended learning technologies increased the students" engagement with their learning

Second-order phase transition in phytoplankton trait dynamics

Key traits of unicellular species, such as cell size, often follow scale-free or self-similar distributions, hinting at the possibility of an underlying critical process. However, linking such empirical scaling laws to the critical regime of realistic individual-based model classes is difficult. Here, we reveal new empirical scaling evidence associated with a transition in the population and the chlorophyll dynamics of phytoplankton. We offer a possible explanation for these observations by deriving scaling laws in the vicinity of the critical point of a new universality class of non-local cell growth and division models. This “criticality hypothesis” can be tested through new scaling predictions derived for our model class, for the response of chlorophyll distributions to perturbations. The derived scaling laws may also be generalized to other cellular traits and environmental drivers relevant to phytoplankton ecology

Clustering: How much bias do we need?

Scientific investigations in medicine and beyond increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing

Big data naturally rescaled

We propose that a handle could be put on big data by looking at the systems that actually generate the data, rather than the data itself, realizing that there may be only few generic processes involved in this, each one imprinting its very specific structures in the space of systems, the traces of which translate into feature space. From this, we propose a practical computational clustering approach, optimized for coping with such data, inspired by how the human cortex is known to approach the problem

Supplementary figures and data file descriptions from Clustering: how much bias do we need?

Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing

The Valencia consensus-based adaptation of the IASP complex regional pain syndrome diagnostic criteria

The new IASP diagnostic criteria for complex regional pain syndrome (CRPS) (aka “the Budapest Criteria”3; Table 1) have improved the diagnostic specificity for CRPS while maintaining good sensitivity. Internationally, these criteria are now in common use. The IASP CRPS Special Interest Group convened a workshop of CRPS experts in Valencia/Spain in September 2019 to review perceived ambiguities in the diagnostic text and issues identified in applying these criteria in both the research and clinical contexts. After this review, workshop attendees discussed and reached a consensus regarding adaptations to the diagnostic taxonomy text. This process resulted in pragmatic updates to CRPS assessment instructions and the associated text in the IASP taxonomy. The wording of the diagnostic criteria themselves was not altered so as to avoid invalidating the criteria